Systems that perform automatic speech recognition (ASR) need to cope with a lot of variability in audio recordings that are processed. For example, the variability can involve different acoustic conditions related to the audio recordings. Often performance of ASR systems with an audio recording having specific characteristics (e.g., certain acoustic conditions) can be improved by using certain operating parameters that are appropriate for the specific characteristics. For example, transcription of audio having specific acoustic properties may be improved by applying various preprocessing procedures such as using certain digital signal processing (DSP) functions, applying certain audio filters, and/or using specific equalizer settings.
However, determining which operating parameters for an ASR system give best results for a certain audio recording is not straightforward. Furthermore, an inappropriate selection of operating parameters may not improve, and may even worsen, the performance of the ASR system. Thus, there is a need for a way to select appropriate operational parameters for an ASR system.